{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d15d8294-3328-4e07-ad16-8a03e9bbfdb9",
   "metadata": {},
   "source": [
    "# YOUR FIRST LAB\n",
    "### Please read this section. This is valuable to get you prepared, even if it's a long read -- it's important stuff.\n",
    "\n",
    "### Also, be sure to read [README.md](../README.md)! More info about the updated videos in the README and [top of the course resources in purple](https://edwarddonner.com/2024/11/13/llm-engineering-resources/)\n",
    "\n",
    "## Your first Frontier LLM Project\n",
    "\n",
    "By the end of this course, you will have built an autonomous Agentic AI solution with 7 agents that collaborate to solve a business problem. All in good time! We will start with something smaller...\n",
    "\n",
    "Our goal is to code a new kind of Web Browser. Give it a URL, and it will respond with a summary. The Reader's Digest of the internet!!\n",
    "\n",
    "Before starting, you should have completed the setup linked in the README.\n",
    "\n",
    "### If you're new to working in \"Notebooks\" (also known as Labs or Jupyter Lab)\n",
    "\n",
    "Welcome to the wonderful world of Data Science experimentation! Simply click in each \"cell\" with code in it, such as the cell immediately below this text, and hit Shift+Return to execute that cell. Be sure to run every cell, starting at the top, in order.\n",
    "\n",
    "Please look in the [Guides folder](../guides/01_intro.ipynb) for all the guides.\n",
    "\n",
    "## I am here to help\n",
    "\n",
    "If you have any problems at all, please do reach out.  \n",
    "I'm available through the platform, or at ed@edwarddonner.com, or at https://www.linkedin.com/in/eddonner/ if you'd like to connect (and I love connecting!)  \n",
    "And this is new to me, but I'm also trying out X at [@edwarddonner](https://x.com/edwarddonner) - if you're on X, please show me how it's done 😂  \n",
    "\n",
    "## More troubleshooting\n",
    "\n",
    "Please see the [troubleshooting](../setup/troubleshooting.ipynb) notebook in the setup folder to diagnose and fix common problems. At the very end of it is a diagnostics script with some useful debug info.\n",
    "\n",
    "## If this is old hat!\n",
    "\n",
    "If you're already comfortable with today's material, please hang in there; you can move swiftly through the first few labs - we will get much more in depth as the weeks progress. Ultimately we will fine-tune our own LLM to compete with OpenAI!\n",
    "\n",
    "<table style=\"margin: 0; text-align: left;\">\n",
    "    <tr>\n",
    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
    "            <img src=\"../assets/important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
    "        </td>\n",
    "        <td>\n",
    "            <h2 style=\"color:#900;\">Please read - important note</h2>\n",
    "            <span style=\"color:#900;\">The way I collaborate with you may be different to other courses you've taken. I prefer not to type code while you watch. Rather, I execute Jupyter Labs, like this, and give you an intuition for what's going on. My suggestion is that you carefully execute this yourself, <b>after</b> watching the lecture. Add print statements to understand what's going on, and then come up with your own variations. If you have a Github account, use this to showcase your variations. Not only is this essential practice, but it demonstrates your skills to others, including perhaps future clients or employers...</span>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>\n",
    "<table style=\"margin: 0; text-align: left;\">\n",
    "    <tr>\n",
    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
    "            <img src=\"../assets/resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
    "        </td>\n",
    "        <td>\n",
    "            <h2 style=\"color:#f71;\">This code is a live resource - keep an eye out for my emails</h2>\n",
    "            <span style=\"color:#f71;\">I push updates to the code regularly. As people ask questions, I add more examples or improved commentary. As a result, you'll notice that the code below isn't identical to the videos. Everything from the videos is here; but I've also added better explanations and new models like DeepSeek. Consider this like an interactive book.<br/><br/>\n",
    "                I try to send emails regularly with important updates related to the course. You can find this in the 'Announcements' section of Udemy in the left sidebar. You can also choose to receive my emails via your Notification Settings in Udemy. I'm respectful of your inbox and always try to add value with my emails!\n",
    "            </span>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>\n",
    "<table style=\"margin: 0; text-align: left;\">\n",
    "    <tr>\n",
    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
    "            <img src=\"../assets/business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
    "        </td>\n",
    "        <td>\n",
    "            <h2 style=\"color:#181;\">Business value of these exercises</h2>\n",
    "            <span style=\"color:#181;\">A final thought. While I've designed these notebooks to be educational, I've also tried to make them enjoyable. We'll do fun things like have LLMs tell jokes and argue with each other. But fundamentally, my goal is to teach skills you can apply in business. I'll explain business implications as we go, and it's worth keeping this in mind: as you build experience with models and techniques, think of ways you could put this into action at work today. Please do contact me if you'd like to discuss more or if you have ideas to bounce off me.</span>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83f28feb",
   "metadata": {},
   "source": [
    "### If necessary, install Cursor Extensions\n",
    "\n",
    "1. From the View menu, select Extensions\n",
    "2. Search for Python\n",
    "3. Click on \"Python\" made by \"ms-python\" and select Install if not already installed\n",
    "4. Search for Jupyter\n",
    "5. Click on \"Jupyter\" made by \"ms-toolsai\" and select Install of not already installed\n",
    "\n",
    "\n",
    "### Next Select the Kernel\n",
    "\n",
    "Click on \"Select Kernel\" on the Top Right\n",
    "\n",
    "Choose \"Python Environments...\"\n",
    "\n",
    "Then choose the one that looks like `.venv (Python 3.12.x) .venv/bin/python` - it should be marked as \"Recommended\" and have a big star next to it.\n",
    "\n",
    "Any problems with this? Head over to the troubleshooting.\n",
    "\n",
    "### Note: you'll need to set the Kernel with every notebook.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4e2a9393-7767-488e-a8bf-27c12dca35bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "\n",
    "import os\n",
    "from dotenv import load_dotenv\n",
    "from scraper import fetch_website_contents\n",
    "from IPython.display import Markdown, display\n",
    "from openai import OpenAI\n",
    "from pypdf import PdfReader\n",
    "import pikepdf\n",
    "\n",
    "# If you get an error running this cell, then please head over to the troubleshooting notebook!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6900b2a8-6384-4316-8aaa-5e519fca4254",
   "metadata": {},
   "source": [
    "# Connecting to OpenAI (or Ollama)\n",
    "\n",
    "The next cell is where we load in the environment variables in your `.env` file and connect to OpenAI.  \n",
    "\n",
    "If you'd like to use free Ollama instead, please see the README section \"Free Alternative to Paid APIs\", and if you're not sure how to do this, there's a full solution in the solutions folder (day1_with_ollama.ipynb).\n",
    "\n",
    "## Troubleshooting if you have problems:\n",
    "\n",
    "If you get a \"Name Error\" - have you run all cells from the top down? Head over to the Python Foundations guide for a bulletproof way to find and fix all Name Errors.\n",
    "\n",
    "If that doesn't fix it, head over to the [troubleshooting](../setup/troubleshooting.ipynb) notebook for step by step code to identify the root cause and fix it!\n",
    "\n",
    "Or, contact me! Message me or email ed@edwarddonner.com and we will get this to work.\n",
    "\n",
    "Any concerns about API costs? See my notes in the README - costs should be minimal, and you can control it at every point. You can also use Ollama as a free alternative, which we discuss during Day 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "00a351d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_text_from_pdf(pdf_path, pdf_password):\n",
    "    \"\"\"\n",
    "    Extracts text from encrypted or unencrypted PDF files.\n",
    "    If encrypted, decrypts it temporarily using pikepdf.\n",
    "    \"\"\"\n",
    "    print(f\"Reading PDF from: {pdf_path}...\")\n",
    "\n",
    "    if not pdf_password:\n",
    "        print(f\"--- ERROR ---\")\n",
    "        print(f\"No 'PDF_PASSWORD' found in your .env file.\")\n",
    "        print(\"Please add your PAN to the .env file (e.g., PDF_PASSWORD=\\\"ABCDE1234F\\\")\")\n",
    "        return None\n",
    "\n",
    "    try:\n",
    "        # Create temporary decrypted file\n",
    "        temp_path = os.path.splitext(pdf_path)[0] + \"_decrypted.pdf\"\n",
    "\n",
    "        # Try opening with pikepdf (handles AES)\n",
    "        print(\"Attempting decryption using pikepdf...\")\n",
    "        with pikepdf.open(pdf_path, password=pdf_password) as pdf:\n",
    "            pdf.save(temp_path)\n",
    "        print(\"Decryption successful!\")\n",
    "\n",
    "        # Now extract text using pypdf\n",
    "        reader = PdfReader(temp_path)\n",
    "        full_text = \"\"\n",
    "        for page in reader.pages:\n",
    "            full_text += page.extract_text() + \"\\n\"\n",
    "\n",
    "        print(\"✅ PDF text extracted successfully.\")\n",
    "        os.remove(temp_path)  # cleanup\n",
    "        return full_text\n",
    "\n",
    "    except WrongPasswordError:\n",
    "        print(f\"--- ERROR --- Wrong PDF password. Check your PAN in the .env file.\")\n",
    "        return None\n",
    "    except Exception as e:\n",
    "        print(f\"An error occurred while reading the PDF: {e}\")\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7b87cadb-d513-4303-baee-a37b6f938e4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "API key found and looks good so far!\n"
     ]
    }
   ],
   "source": [
    "# Load environment variables in a file called .env\n",
    "\n",
    "load_dotenv(override=True)\n",
    "api_key = os.getenv('OPENAI_API_KEY')\n",
    "pdf_password = os.getenv('PDF_PASSWORD')\n",
    "pdf_file_path = os.getenv(\"PDF_PATH\")\n",
    "\n",
    "# Check the key\n",
    "\n",
    "if not api_key:\n",
    "    print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n",
    "elif not api_key.startswith(\"sk-proj-\"):\n",
    "    print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n",
    "elif api_key.strip() != api_key:\n",
    "    print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n",
    "else:\n",
    "    print(\"API key found and looks good so far!\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "442fc84b-0815-4f40-99ab-d9a5da6bda91",
   "metadata": {},
   "source": [
    "# Let's make a quick call to a Frontier model to get started, as a preview!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a58394bf-1e45-46af-9bfd-01e24da6f49a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To give you a preview -- calling OpenAI with these messages is this easy. Any problems, head over to the Troubleshooting notebook.\n",
    "\n",
    "message = \"Hello, GPT! This is my first ever message to you! Hi!\"\n",
    "\n",
    "messages = [{\"role\": \"user\", \"content\": message}]\n",
    "\n",
    "messages\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08330159",
   "metadata": {},
   "outputs": [],
   "source": [
    "openai = OpenAI()\n",
    "\n",
    "response = openai.chat.completions.create(model=\"gpt-5-nano\", messages=messages)\n",
    "response.choices[0].message.content"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2aa190e5-cb31-456a-96cc-db109919cd78",
   "metadata": {},
   "source": [
    "## OK onwards with our first project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ef960cf-6dc2-4cda-afb3-b38be12f4c97",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's try out this utility\n",
    "\n",
    "ed = fetch_website_contents(\"https://edwarddonner.com\")\n",
    "print(ed)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a478a0c-2c53-48ff-869c-4d08199931e1",
   "metadata": {},
   "source": [
    "## Types of prompts\n",
    "\n",
    "You may know this already - but if not, you will get very familiar with it!\n",
    "\n",
    "Models like GPT have been trained to receive instructions in a particular way.\n",
    "\n",
    "They expect to receive:\n",
    "\n",
    "**A system prompt** that tells them what task they are performing and what tone they should use\n",
    "\n",
    "**A user prompt** -- the conversation starter that they should reply to"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abdb8417-c5dc-44bc-9bee-2e059d162699",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define our system prompt - you can experiment with this later, changing the last sentence to 'Respond in markdown in Spanish.\"\n",
    "\n",
    "system_prompt = \"\"\"\n",
    "You are a funny assistant that analyzes the contents of a website,\n",
    "and provides a short, snarky, humorous summary, ignoring text that might be navigation related.\n",
    "Respond in markdown. Do not wrap the markdown in a code block - respond just with the markdown.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0275b1b-7cfe-4f9d-abfa-7650d378da0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define our user prompt\n",
    "\n",
    "user_prompt_prefix = \"\"\"\n",
    "Here are the contents of a website.\n",
    "Provide a short summary of this website.\n",
    "If it includes news or announcements, then summarize these too.\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea211b5f-28e1-4a86-8e52-c0b7677cadcc",
   "metadata": {},
   "source": [
    "## Messages\n",
    "\n",
    "The API from OpenAI expects to receive messages in a particular structure.\n",
    "Many of the other APIs share this structure:\n",
    "\n",
    "```python\n",
    "[\n",
    "    {\"role\": \"system\", \"content\": \"system message goes here\"},\n",
    "    {\"role\": \"user\", \"content\": \"user message goes here\"}\n",
    "]\n",
    "```\n",
    "To give you a preview, the next 2 cells make a rather simple call - we won't stretch the mighty GPT (yet!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f25dcd35-0cd0-4235-9f64-ac37ed9eaaa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "messages = [\n",
    "    {\"role\": \"system\", \"content\": \"You are a strict militay officer assistant\"},\n",
    "    {\"role\": \"user\", \"content\": \"What is 2 + 2?\"}\n",
    "]\n",
    "\n",
    "response = openai.chat.completions.create(model=\"gpt-4.1-nano\", messages=messages)\n",
    "response.choices[0].message.content"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d06e8d78-ce4c-4b05-aa8e-17050c82bb47",
   "metadata": {},
   "source": [
    "## And now let's build useful messages for GPT-4.1-mini, using a function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0134dfa4-8299-48b5-b444-f2a8c3403c88",
   "metadata": {},
   "outputs": [],
   "source": [
    "# See how this function creates exactly the format above\n",
    "\n",
    "def messages_for(website):\n",
    "    return [\n",
    "        {\"role\": \"system\", \"content\": system_prompt},\n",
    "        {\"role\": \"user\", \"content\": user_prompt_prefix + website}\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36478464-39ee-485c-9f3f-6a4e458dbc9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Try this out, and then try for a few more websites\n",
    "\n",
    "messages_for(ed)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16f49d46-bf55-4c3e-928f-68fc0bf715b0",
   "metadata": {},
   "source": [
    "## Time to bring it together - the API for OpenAI is very simple!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "905b9919-aba7-45b5-ae65-81b3d1d78e34",
   "metadata": {},
   "outputs": [],
   "source": [
    "# And now: call the OpenAI API. You will get very familiar with this!\n",
    "\n",
    "def summarize(url):\n",
    "    website = fetch_website_contents(url)\n",
    "    response = openai.chat.completions.create(\n",
    "        model = \"gpt-4.1-mini\",\n",
    "        messages = messages_for(website)\n",
    "    )\n",
    "    return response.choices[0].message.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05e38d41-dfa4-4b20-9c96-c46ea75d9fb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "summarize(\"https://edwarddonner.com\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d926d59-450e-4609-92ba-2d6f244f1342",
   "metadata": {},
   "outputs": [],
   "source": [
    "# A function to display this nicely in the output, using markdown\n",
    "\n",
    "def display_summary(url):\n",
    "    summary = summarize(url)\n",
    "    display(Markdown(summary))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3018853a-445f-41ff-9560-d925d1774b2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "display_summary(\"https://edwarddonner.com\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3bcf6f4-adce-45e9-97ad-d9a5d7a3a624",
   "metadata": {},
   "source": [
    "# Let's try more websites\n",
    "\n",
    "Note that this will only work on websites that can be scraped using this simplistic approach.\n",
    "\n",
    "Websites that are rendered with Javascript, like React apps, won't show up. See the community-contributions folder for a Selenium implementation that gets around this. You'll need to read up on installing Selenium (ask ChatGPT!)\n",
    "\n",
    "Also Websites protected with CloudFront (and similar) may give 403 errors - many thanks Andy J for pointing this out.\n",
    "\n",
    "But many websites will work just fine!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45d83403-a24c-44b5-84ac-961449b4008f",
   "metadata": {},
   "outputs": [],
   "source": [
    "display_summary(\"https://cnn.com\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75e9fd40-b354-4341-991e-863ef2e59db7",
   "metadata": {},
   "outputs": [],
   "source": [
    "display_summary(\"https://anthropic.com\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "512125a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "display_summary(\"https://www.udemy.com\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c951be1a-7f1b-448f-af1f-845978e47e2c",
   "metadata": {},
   "source": [
    "<table style=\"margin: 0; text-align: left;\">\n",
    "    <tr>\n",
    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
    "            <img src=\"../assets/business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
    "        </td>\n",
    "        <td>\n",
    "            <h2 style=\"color:#181;\">Business applications</h2>\n",
    "            <span style=\"color:#181;\">In this exercise, you experienced calling the Cloud API of a Frontier Model (a leading model at the frontier of AI) for the first time. We will be using APIs like OpenAI at many stages in the course, in addition to building our own LLMs.\n",
    "\n",
    "More specifically, we've applied this to Summarization - a classic Gen AI use case to make a summary. This can be applied to any business vertical - summarizing the news, summarizing financial performance, summarizing a resume in a cover letter - the applications are limitless. Consider how you could apply Summarization in your business, and try prototyping a solution.</span>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>\n",
    "\n",
    "<table style=\"margin: 0; text-align: left;\">\n",
    "    <tr>\n",
    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
    "            <img src=\"../assets/important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
    "        </td>\n",
    "        <td>\n",
    "            <h2 style=\"color:#900;\">Before you continue - now try yourself</h2>\n",
    "            <span style=\"color:#900;\">Use the cell below to make your own simple commercial example. Stick with the summarization use case for now. Here's an idea: write something that will take the contents of an email, and will suggest an appropriate short subject line for the email. That's the kind of feature that might be built into a commercial email tool.</span>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "00743dac-0e70-45b7-879a-d7293a6f68a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading PDF from: C:\\Users\\jaasm\\Downloads\\28-10-25 trade.pdf...\n",
      "Attempting decryption using pikepdf...\n",
      "Decryption successful!\n",
      "✅ PDF text extracted successfully.\n",
      "Connecting to OpenAI to get your summary...\n",
      "\n",
      "--- 📈 Your Trading Summary ---\n",
      "# Trading Summary\n",
      "\n",
      "**Total Turnover**: ₹11,577.50\n",
      "\n",
      "**Itemized Charges**:\n",
      "- **Brokerage**: ₹40.00\n",
      "- **Securities Transaction Tax (STT)**: ₹14.00\n",
      "- **Exchange Transaction Charges**: ₹6.54\n",
      "- **GST (IGST)**: ₹19.18\n",
      "- **SEBI Turnover Fees**: ₹0.02\n",
      "- **Stamp Duty**: ₹0.00\n",
      "\n",
      "**Total Charges**: ₹79.74\n",
      "\n",
      "**Net Realized Profit or Loss**: **Loss** of ₹4,123.74\n"
     ]
    }
   ],
   "source": [
    "# Step 1: Create your prompts\n",
    "\n",
    "system_prompt = \"\"\"\n",
    "You are a specialized financial assistant designed to analyze and summarize daily trading reports from Zerodha. \n",
    "Your primary function is to extract key financial figures from the text of a contract note provided by the user and present them in a clear, \n",
    "concise, and structured summary.\n",
    "\n",
    "Your response MUST include the following, calculated from the provided text:\n",
    "1.  **Total Turnover**: The sum of all buy and sell transaction values.\n",
    "2.  **Itemized Charges**: A breakdown of all taxes and fees. This includes Brokerage, STT (Securities Transaction Tax), \n",
    "Exchange Transaction Charges, GST, SEBI Turnover Fees, and Stamp Duty.\n",
    "3.  **Total Charges**: The sum of all the itemized charges.\n",
    "4.  **Net Realized Profit or Loss**: The final profit or loss after all charges have been deducted. Clearly label it as 'Profit' or 'Loss'.\n",
    "\n",
    "Respond ONLY in Markdown format. Use headings and bolding for clarity. Do not include any conversational phrases, greetings, or explanations.\n",
    "\"\"\"\n",
    "\n",
    "user_prompt = extract_text_from_pdf(pdf_file_path, pdf_password)\n",
    "\n",
    "# Step 2: Make the messages list\n",
    "if user_prompt:\n",
    "    messages = [\n",
    "        {\"role\": \"system\", \"content\": system_prompt},\n",
    "        {\"role\": \"user\", \"content\": user_prompt}\n",
    "    ] # fill this in\n",
    "\n",
    "    # Step 3: Call OpenAI\n",
    "    print(\"Connecting to OpenAI to get your summary...\")\n",
    "    try:\n",
    "        openai_client = OpenAI()\n",
    "        response = openai_client.chat.completions.create(\n",
    "            model=\"gpt-4o-mini\",  # gpt-4o is excellent for this\n",
    "            messages=messages\n",
    "        )\n",
    "\n",
    "        # Step 4: print the result\n",
    "        print(\"\\n--- 📈 Your Trading Summary ---\")\n",
    "        print(response.choices[0].message.content)\n",
    "\n",
    "    except Exception as e:\n",
    "        print(f\"--- ERROR ---\")\n",
    "        print(f\"An error occurred while contacting OpenAI: {e}\")"
   ]
  },
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    "## An extra exercise for those who enjoy web scraping\n",
    "\n",
    "You may notice that if you try `display_summary(\"https://openai.com\")` - it doesn't work! That's because OpenAI has a fancy website that uses Javascript. There are many ways around this that some of you might be familiar with. For example, Selenium is a hugely popular framework that runs a browser behind the scenes, renders the page, and allows you to query it. If you have experience with Selenium, Playwright or similar, then feel free to improve the Website class to use them. In the community-contributions folder, you'll find an example Selenium solution from a student (thank you!)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eeab24dc-5f90-4570-b542-b0585aca3eb6",
   "metadata": {},
   "source": [
    "# Sharing your code\n",
    "\n",
    "I'd love it if you share your code afterwards so I can share it with others! You'll notice that some students have already made changes (including a Selenium implementation) which you will find in the community-contributions folder. If you'd like add your changes to that folder, submit a Pull Request with your new versions in that folder and I'll merge your changes.\n",
    "\n",
    "If you're not an expert with git (and I am not!) then GPT has given some nice instructions on how to submit a Pull Request. It's a bit of an involved process, but once you've done it once it's pretty clear. As a pro-tip: it's best if you clear the outputs of your Jupyter notebooks (Edit >> Clean outputs of all cells, and then Save) for clean notebooks.\n",
    "\n",
    "Here are good instructions courtesy of an AI friend:  \n",
    "https://chatgpt.com/share/677a9cb5-c64c-8012-99e0-e06e88afd293"
   ]
  },
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